vLLM Reaches 25K Total TPS/GPU on Qwen3.5

Key takeaways

How to use this in real systems

1. 1.Teams can optimize their AI model serving by implementing disaggregated serving architectures, which can significantly increase throughput and resource utilization in production environments.
2. 2.Incorporating advanced scheduling techniques and cache transfer mechanisms, such as those described in the vLLM framework, can enhance the efficiency of GPU resource management, leading to improved performance in real-time applications.
3. 3.Adopting the latest kernel optimizations and recipes, like Blackwell GDN and srt-slurm, can help engineering teams streamline deployment processes and reduce latency, ensuring that AI services meet operational demands effectively.